QoS-Aware and Resource-Efficient Dynamic Slicing Mechanism for Internet of Things

He, Wenchen; Guo, Shaoyong; Liang, Yun; Ma, Rui; Qiu, Xuesong; Shi, Lei

doi:10.32604/cmc.2019.06669

Cited by 5 publications

(4 citation statements)

References 13 publications

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“…e method can help users use resources rationally, reduce the cost of managing infrastructure, and improve the quality of 4 Complexity life of consumers. He et al [33] proposed a dynamic network slice strategy. e network slice can be dynamically adjusted according to the time-varying resource demands.…”

Section: 15mentioning

confidence: 99%

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Wang

Sun

et al. 2020

Complexity

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With an increase of service users’ demands on high quality of services (QoS), more and more efficient service computing models are proposed. The development of cloud computing, fog computing, and edge computing brings a number of challenges, e.g., QoS optimization and energy saving. We do a comprehensive survey on QoS optimization and energy saving in cloud computing, fog computing, edge computing, and IoT environments. We summarize the main challenges and analyze corresponding solutions proposed by existing works. This survey aims to help readers have a deeper understanding on the concepts of different computing models and study the techniques of QoS optimization and energy saving in these models.

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Section: 15mentioning

confidence: 99%

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Wang

Sun

et al. 2020

Complexity

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“…These proposed techniques can deal with the heterogeneity of resources in the fog computing environment. He et al [17] propose a dynamic network slicing mechanism including virtual network mapping and reconfiguration to provide network slices for services. Liu et al [18] present a service scheduling method, that includes the architecture, components and functional requirements, to balance the business load of Power Distribution Internet of Things based on edge computing.…”

Section: Related Work and Comparison A Iot Service Allocation And Compositionmentioning

confidence: 99%

Optimized Composition for Multiple User Service Requests Based on Edge-Cloud Collaboration

Tang

Lin

Wang³

et al. 2021

IEEE Access

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The Internet of Things (IoT) has become the infrastructure to widely support ubiquitous services. In this setting, a large number of smart object devices provide IoT services for people to carry on day-to-day activities. These services are easy to access, and thus, can be easily used by different users with different requirements through service allocation and composition. Nevertheless, with the increasing adoption and presence of IoT services, it becomes more difficult to find the appropriate IoT service compositions that can maximize the service utility of all the users' requests. Moreover, the IoT devices are usually resource constrained and cannot effectively finish some complex task of service requests, thereby reducing the service quality. To address this problem, we develop a novel IoT service allocation mechanism in collaborative device-edge-cloud architecture. First, prevalence of frequent function interaction is mined to help construct IoT service allocation index. Second, service requests' priority and their preferences towards branch nodes are further set and estimated to help maximize the service composition utility of multiple user requests. Third, edge device is exploited to help service execution and guarantee service allocation utility by offloading task from IoT devices. The results are demonstrated by conducting a set of extensive experiments, which show that the proposed method performs better than the alternative techniques in reducing the total energy consumption and promoting the service utility maximum.

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“…At present, the research on sensor networks involves sensor network routing protocol [1][2][3], sensor network security [4,5], internet of things resource scheduling [6,7], energy consumption management [8] and so on. However, there is little research on sensor network structure recognition.…”

Section: Introductionmentioning

confidence: 99%

Sensor Network Structure Recognition Based on P-law

You¹,

Lin²,

Qiu³

et al. 2023

Computer Systems Science and Engineering

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A sensor graph network is a sensor network model organized according to graph network structure. Structural unit and signal propagation of core nodes are the basic characteristics of sensor graph networks. In sensor networks, network structure recognition is the basis for accurate identification and effective prediction and control of node states. Aiming at the problems of difficult global structure identification and poor interpretability in complex sensor graph networks, based on the characteristics of sensor networks, a method is proposed to firstly unitize the graph network structure and then expand the unit based on the signal transmission path of the core node. This method which builds on unit patulousness and core node signal propagation (called p-law) can rapidly and effectively achieve the global structure identification of a sensor graph network. Different from the traditional graph network structure recognition algorithms such as modularity maximization and spectral clustering, the proposed method reveals the natural evolution process and law of graph network subgroup generation. Experimental results confirm the effectiveness, accuracy and rationality of the proposed method and suggest that our method can be a new approach for graph network global structure recognition.

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QoS-Aware and Resource-Efficient Dynamic Slicing Mechanism for Internet of Things

Cited by 5 publications

References 13 publications

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Optimized Composition for Multiple User Service Requests Based on Edge-Cloud Collaboration

Sensor Network Structure Recognition Based on P-law

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